Ruminal Microbiome Differences in Angus Steers with Differing Feed Efficiencies during the Feedlot Finishing Phase.

The catabolic activity of the ruminal microbial community of cattle enables the conversion of low-quality feedstuffs into meat and milk. The rate at which this conversion occurs is termed feed efficiency, which is of crucial importance given that feed expenses account for up to 70% of the cost of animal production. The present study assessed the relationship between cattle feed efficiency and the composition of their ruminal microbial communities during the feedlot finishing period. Angus steers (n = 65) were fed a feedlot finishing diet for 82 days and their growth performance metrics were evaluated. These included the dry matter intake (DMI), average daily gain (ADG), and residual feed intake (RFI). Steers were rank-ordered based upon their RFI, and the five lowest RFI (most efficient) and five highest RFI (least efficient) steers were selected for evaluations. Ruminal fluid samples were collected on days 0 and 82 of the finishing period. Volatile fatty acids (VFA) were quantified, and microbial DNA was extracted and the 16S rRNA gene was sequenced. The results showed that the ADG was not different (p = 0.82) between efficiency groups during the 82-day feedlot period; however, the efficient steers had lower (p = 0.03) DMI and RFI (p = 0.003). Less-efficient (high RFI) steers developed higher (p = 0.01) ruminal Methanobrevibacter relative abundances (p = 0.01) and tended (p = 0.09) to have more Methanosphaera. In high-efficiency steers (low RFI), the relative abundances of Ruminococcaceae increased (p = 0.04) over the 82-day period. The molar proportions of VFA were not different between the two efficiency groups, but some changes in the concentration of specific VFA were observed over time. The results indicated that the ruminal microbial populations of the less-efficient steers contained a greater relative abundance of methanogens compared to the high-efficiency steers during the feedlot phase, likely resulting in more energetic waste in the form or methane and less dietary energy being harvested by the less-efficient animals.

Alternative Additives for Organic and Natural Ready-to-Eat Meats to Control Spoilage and Maintain Shelf Life: Current Perspectives in the United States.

Food additives are employed in the food industry to enhance the color, smell, and taste of foods, increase nutritional value, boost processing efficiency, and extend shelf life. Consumers are beginning to prioritize food ingredients that they perceive as supporting a healthy lifestyle, emphasizing ingredients they deem acceptable as alternative or "clean-label" ingredients. Ready-to-eat (RTE) meat products can be contaminated with pathogens and spoilage microorganisms after the cooking step, contributing to food spoilage losses and increasing the risk to consumers for foodborne illnesses. More recently, consumers have advocated for no artificial additives or preservatives, which has led to a search for antimicrobials that meet these demands but do not lessen the safety or quality of RTE meats. Lactates and diacetates are used almost universally to extend the shelf life of RTE meats by reducing spoilage organisms and preventing the outgrowth of the foodborne pathogen Listeria monocytogenes. These antimicrobials applied to RTE meats tend to be broad-spectrum in their activities, thus affecting overall microbial ecology. It is to the food processing industry's advantage to target spoilage organisms and pathogens specifically.

Genotypic characterization, antimicrobial susceptibility and virulence determinants of Campylobacter jejuni and Campylobacter coli isolated from pastured poultry farms.

Aim: Campylobacter is the leading bacterial pathogen that causes foodborne illnesses worldwide. Pasture farming is regarded as an important source of agricultural production for small farming communities. Consumer preference for pasture-raised animal products has increased; however, there is a paucity of information on the microbiological quality of pasture-raised poultry products. The purpose of this study was to explore genetic relatedness of thermophilic Campylobacter isolates, to assess antibiotic resistance phenotypically and genotypically, and to screen the presence of virulence determinants of Campylobacter isolates from pasture-raised poultry farms from southeastern United States. Methods: Ninety-seven Campylobacter isolates previously identified by Q7 BAX® System Real-Time PCR were genotyped by multilocus sequence typing (MLST). Campylobacter isolates were then evaluated for their phenotypic antimicrobial susceptibility against nine antimicrobial agents using Sensititre plates. Additionally, Campylobacter isolates were tested for the presence of antimicrobial resistance-associated elements. Furthermore, Campylobacter isolates were screened for the presence of 13 genes encoding putative virulence factors by PCR. These included genes involved in motility (flaA and flhA), adhesion and colonization (cadF, docC, racR, and virB11), toxin production (cdtA, cdtB, cdtC, wlaN, and ceuE) and invasion (ciaB and iamA). Results: Among 97 Campylobacter isolates, Campylobacter jejuni (n = 79) and Campylobacter coli (n = 18) were identified. By MLST, C. jejuni isolates were assigned to seven clonal complexes. Among them, ST-353, ST-607 and ST-21 were the most common STs recognized. All C. coli (n = 18) isolates were included in CC-828. Interestingly, eight STs identified were not belonging any previous identified clonal complex. Campylobacter isolates displayed a high resistance rate against tetracycline (81.4%), while a low rate of resistance was observed against macrolides (azithromycin and erythromycin), quinolones and fluoroquinolones (nalidixic acid and ciprofloxacin), aminoglycosides (gentamicin), ketolide (telithromycin), amphenicol (florfenicol) and lincomycin (clindamycin). Thirteen isolates (13.54%) were pan-susceptible to all tested antibiotics, while nine isolates were multi-antimicrobial resistant (MAR; resist to three or more antimicrobial classes). Interestingly, there were no isolates resistant to all antimicrobial classes. Thr86Ile mutation was identified in all quinolones resistant strains. Erythromycin encoding gene (ermB) was identified in 75% of erythromycin resistant isolates. The A2075 mutation was detected in one erythromycin resistant strain, while A2074 could not be identified. The tetO gene was identified in 93.7% of tetracycline resistant isolates and six tetracycline susceptible isolates. In conclusion, the results of this study revealed that Campylobacter isolates from pasture-raised poultry farms showed the ST relatedness to Campylobacter isolates commonly associated with humans, indicating pasture-raised broiler flocks, similar to conventionally-reared broiler flocks, as a potential vector for antibiotic-resistant and pathogenic strains of thermophilic Campylobacter to humans.

Predicting foodborne pathogens and probiotics taxa within poultry-related microbiomes using a machine learning approach.

BACKGROUND: Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely influenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Probiotics are beneficial living microorganisms capable of improving the host organism's digestive system, immune function and ultimately overall health. Here, 16 S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 42 pastured poultry flocks (poultry in this entire work solely refers to chickens) from southeastern U.S. farms was used to generate the relative abundance of operational taxonomic units (OTUs) as machine learning input. Unique genera from the OTUs were used as predictors of the prevalence of foodborne pathogens (Salmonella, Campylobacter and Listeria) at different stages of poultry growth (START (2-4 weeks old), MID (5-7 weeks old), END (8-11 weeks old)), association with farm management practices and physicochemical properties. RESULT: While we did not see any significant associations between known probiotics and Salmonella or Listeria, we observed significant negative correlations between known probiotics (Bacillus and Clostridium) and Campylobacter at the mid-time point of sample collection. Our data indicates a negative correlation between potential probiotics and Campylobacter at both early and end-time points of sample collection. Furthermore, our model prediction shows that changes in farm operations such as how often the houses are moved on the pasture, age at which chickens are introduced to the pasture, diet composition and presence of other animals on the farm could favorably increase the abundance and activity of probiotics that could reduce Campylobacter prevalence. CONCLUSION: Integration of microbiome data with farm management practices using machine learning provided insights on how to reduce Campylobacter prevalence and transmission along the farm-to-fork continuum. Altering management practices to support proliferation of beneficial probiotics to reduce pathogen prevalence identified here could constitute a complementary method to the existing but ineffective interventions such as vaccination and bacteriophage cocktails usage. Study findings also corroborate the presence of bacterial genera such as Caloramator, DA101, Parabacteroides and Faecalibacterium as potential probiotics.

Comparison of optical density-based growth kinetics for pure culture Campylobacter jejuni, coli and lari grown in blood-free Bolton broth.

Campylobacter growth kinetic parameters can be used to refine the sensitivity and efficiency of microbial growth-based methods. Therefore, the aim of this study was to construct growth curves for C. jejuni, C. coli, and C. lari in pure culture and calculate growth kinetics for each Campylobacter species in the same environmental conditions. Campylobacter jejuni, C. coli and C. lari were grown over 48 h and inoculated into 15 mL Hungate tubes (N = 3 trials per species; 5 biological replicates per trial; 3 species; 1 strain per species). Absorbance measurements were taken in 45 min intervals over 24 h. Optical density readings were plotted versus time to calculate growth kinetic parameters. C. jejuni exhibited the longest lag phase (p < 0.001) at 15 h 20 min ± 30 min, versus C. coli at 11 h 15 min ± 17 min, and C. lari at 9 h 27 min ± 15 min. The exponential phase duration was no longer than 5 h for all species, and doubling times were all less than 1h 30 min. The variation in growth kinetics for the three species of Campylobacter illustrates the importance of determining individual Campylobacter spp. growth responses for optimizing detection based on low bacterial levels. This study provides kinetics and estimates to define enrichment times necessary for low concentration Campylobacter detection.

Listeria Occurrence in Conventional and Alternative Egg Production Systems.

Listeria continues to be a persistent foodborne pathogen that is responsible for human cases of listeriosis when contaminated food products are consumed. Human subjects considered to be most susceptible include the elderly, immunocompromised, and pregnant women. Listeria is characterized as a saprophytic organism with the capability of responding and adapting to constantly changing environments because it possesses multiple stress response mechanisms to overcome varying temperatures, salt concentrations, and pH, among others. Primary foods and food products associated with listeriosis include dairy products and ready-to-eat meats such as turkey products. Historically, chicken eggs have not been identified as a primary source of Listeria, but the potential for contamination during egg production and processing does exist. Listeria species have been isolated from egg-processing plant equipment and are presumed to occur in egg-processing plant environments. Whether Listeria is consistently disseminated onto eggs beyond the egg-processing plant is a risk factor that remains to be determined. However, research has been conducted over the years to develop egg wash solutions that generate combinations of pH and other properties that would be considered inhibitory to Listeria. Even less is known regarding the association of Listeria with alternative egg production systems, but Listeria has been isolated from pasture flock broilers, so it is conceivable, given the nature of the outdoor environments, that layer birds under these conditions would also be exposed to Listeria and their eggs become contaminated. This review focuses on the possibility of Listeria occurring in conventional and alternative egg-laying production and processing systems.

Relationship of the Poultry Microbiome to Pathogen Colonization, Farm Management, Poultry Production, and Foodborne Illness Risk Assessment.

Despite the continuous progress in food science and technology, the global burden of foodborne illnesses remains substantial, with pathogens in food causing millions of infections each year. Traditional microbiological culture methods are inadequate in detecting the full spectrum of these microorganisms, highlighting the need for more comprehensive detection strategies. This review paper aims to elucidate the relationship between foodborne pathogen colonization and the composition of the poultry microbiome, and how this knowledge can be used for improved food safety. Our review highlights that the relationship between pathogen colonization varies across different sections of the poultry microbiome. Further, our review suggests that the microbiome profile of poultry litter, farm soil, and farm dust may serve as potential indicators of the farm environment's food safety issues. We also agree that the microbiome of processed chicken samples may reveal potential pathogen contamination and food quality issues. In addition, utilizing predictive modeling techniques on the collected microbiome data, we suggest establishing correlations between particular taxonomic groups and the colonization of pathogens, thus providing insights into food safety, and offering a comprehensive overview of the microbial community. In conclusion, this review underscores the potential of microbiome analysis as a powerful tool in food safety, pathogen detection, and risk assessment.

Assessing the Risk of Seasonal Effects of Campylobacter Contaminated Broiler Meat Prepared In-Home in the United States.

Campylobacter has consistently posed a food safety issue in broiler meat. This study aimed to create a quantitative microbial risk assessment model from retail to consumption, designed to evaluate the seasonal risk of campylobacteriosis associated with broiler meat consumption in the United States. To achieve this, data was gathered to build distributions that would enable us to predict the growth of Campylobacter during various stages such as retail storage, transit, and home storage. The model also included potential fluctuations in concentration during food preparation and potential cross-contamination scenarios. A Monte Carlo simulation with 100,000 iterations was used to estimate the risk of infection per serving and the number of infections in the United States by season. In the summer, chicken meat was estimated to have a median risk of infection per serving of 9.22 × 10-7 and cause an average of about 27,058,680 infections. During the winter months, the median risk of infection per serving was estimated to be 4.06 × 10-7 and cause an average of about 12,085,638 infections. The risk assessment model provides information about the risk of broiler meat to public health by season. These results will help understand the most important steps to reduce the food safety risks from contaminated chicken products.

Tanshinone IIA-loaded nanoparticles and neural stem cell combination therapy improves gut homeostasis and recovery in a pig ischemic stroke model.

Impaired gut homeostasis is associated with stroke often presenting with leaky gut syndrome and increased gut, brain, and systemic inflammation that further exacerbates brain damage. We previously reported that intracisternal administration of Tanshinone IIA-loaded nanoparticles (Tan IIA-NPs) and transplantation of induced pluripotent stem cell-derived neural stem cells (iNSCs) led to enhanced neuroprotective and regenerative activity and improved recovery in a pig stroke model. We hypothesized that Tan IIA-NP + iNSC combination therapy-mediated stroke recovery may also have an impact on gut inflammation and integrity in the stroke pigs. Ischemic stroke was induced, and male Yucatan pigs received PBS + PBS (Control, n = 6) or Tan IIA-NP + iNSC (Treatment, n = 6) treatment. The Tan IIA-NP + iNSC treatment reduced expression of jejunal TNF-α, TNF-α receptor1, and phosphorylated IkBα while increasing the expression of jejunal occludin, claudin1, and ZO-1 at 12 weeks post-treatment (PT). Treated pigs had higher fecal short-chain fatty acid (SCFAs) levels than their counterparts throughout the study period, and fecal SCFAs levels were negatively correlated with jejunal inflammation. Interestingly, fecal SCFAs levels were also negatively correlated with brain lesion volume and midline shift at 12 weeks PT. Collectively, the anti-inflammatory and neuroregenerative treatment resulted in increased SCFAs levels, tight junction protein expression, and decreased inflammation in the gut.

An ensemble learning approach to identify pastured poultry farm practice variables and soil constituents that promote Salmonella prevalence.

Animal sourced foods including contaminated poultry meat and eggs contribute to human non-typhoidal salmonellosis, a foodborne zoonosis. Prevalence of Salmonella in pastured poultry production systems can lead to contamination of the final product. Identification of farm practices that affect Salmonella prevalence is critical for implementing control measures to ensure the safety of these products. In this study, we developed predictive models based predominantly on deep learning approaches to identify key pre-harvest management variables (using soil and feces samples) in pastured poultry farms that contribute to Salmonella prevalence. Our ensemble approach utilizing five different machine learning techniques predicts that physicochemical parameters of the soil and feces (elements such as sodium (Na), zinc (Zn), potassium (K), copper (Cu)), electrical conductivity (EC), the number of years that the farms have been in use, and flock size significantly influence pre-harvest Salmonella prevalence. Egg source, feed type, breed, and manganese (Mn) levels in the soil/feces are other important variables identified to contribute to Salmonella prevalence on larger (≥3 flocks reared per year) farms, while pasture feed and soil carbon-to-nitrogen ratio are predicted to be important for smaller/hobby (<3 flocks reared per year) farms. Predictive models such as the ones described here are important for developing science-based control measures for Salmonella to reduce the environmental, animal, and public health impacts from these types of poultry production systems.

Comparison between LASSO and RT methods for prediction of generic E. coli concentration in pastured poultry farms.

Though most strains of E. coli are non-pathogenic components of the intestinal microbiome, certain pathogenic E. coli strains are the cause of diseases and outbreaks. Poultry is identified as a common reservoir for pathogenic E. coli. It is important to identify farm practice factors associated with E. coli in the pastured poultry environment. The objective of this study is to develop models that can predict E. coli levels and to select farm practice factors contributing to E. coli concentration in pastured poultry farms. Five kinds of samples: feces, soil, whole carcass rinse after processing (WCR-P), final product after chilling and storage (WCR-F), and ceca samples were collected for E. coli counts from 11 pastured poultry farms in the southeastern US. The regression tree (RT) and least absolute shrinkage and selection operator (LASSO) methods were applied to data from each sample type. The farm management practices and processing factors such as source of eggs, type of feed used, appearance of other animals on farm, chilling method, and storage time and temperature were all considered as possible explanatory factors in the models. Models were developed to predict the levels of E. coli and to select the most important factors used in predicting E. coli population. Model performances were compared using root mean square error (RMSE). For feces samples, average number of birds and animal source were the two most important variables affecting E. coli population by LASSO. The RT selected animal source, brood feed, day of year, flock age in days, and flock size as the most important variables in predicting E. coli concentration. The RMSE (in log10 scale) under LASSO was 0.974, while under RT it was 1.032 for feces samples. The predictive models provide practical and effective tools to predict E. coli population and to identify farm practice factors that affect E. coli levels.

Preharvest Environmental and Management Drivers of Multidrug Resistance in Major Bacterial Zoonotic Pathogens in Pastured Poultry Flocks.

Due to nutritional benefits and perceived humane ways of treating the animals, the demand for antibiotic-free pastured poultry chicken has continued to be steadily rise. Despite the non-usage of antibiotics in pastured poultry broiler production, antibiotic resistance (AR) is reported in zoonotic poultry pathogens. However, factors that drive multidrug resistance (MDR) in pastured poultry are not well understood. In this study, we used machine learning and deep learning approaches to predict farm management practices and physicochemical properties of feces and soil that drive MDR in zoonotic poultry pathogens. Antibiotic use in agroecosystems is known to contribute to resistance. Evaluation of the development of resistance in environments that are free of antibiotics such as the all-natural, antibiotic-free, pastured poultry production systems described here is critical to understand the background AR in the absence of any selection pressure, i.e., basal levels of resistance. We analyzed 1635 preharvest (feces and soil) samples collected from forty-two pastured poultry flocks and eleven farms in the Southeastern United States. CDC National Antimicrobial Resistance Monitoring System guidelines were used to determine antimicrobial/multidrug resistance profiles of Salmonella, Listeria, and Campylobacter. A combination of two traditional machine learning (RandomForest and XGBoost) and three deep learning (Multi-layer Perceptron, Generative Adversarial Network, and Auto-Encoder) approaches identified critical farm management practices and environmental variables that drive multidrug resistance in poultry pathogens in broiler production systems that represents background resistance. This study enumerates management practices that contribute to AR and makes recommendations to potentially mitigate multidrug resistance and the prevalence of Salmonella and Listeria in pastured poultry.

Using E. coli population to predict foodborne pathogens in pastured poultry farms.

Escherichia coli shows the potential of indicating foodborne pathogens. The purpose of this study is to investigate the association between E. coli and foodborne pathogens such as Campylobacter, Salmonella, and Listeria in pastured poultry farms, as well as in related processing facilities. Five different sample types: (i) feces, (ii) soil, (iii) whole carcass rinse during processing (WCR-P), (iv) whole carcass rinse of final product after chilling and storage (WCR-F), and (v) ceca were measured for E. coli populations. A logistic regression model for pathogen presence was developed for each sample type. The E. coli population significantly increased the predicted probability of Salmonella presence for soil and WCR-P samples (p = 0.0011 and p = 0.0157 respectively). For Campylobacter, the initial prevalence in feces and ceca were high and a decreasing trend of detecting Campylobacter was observed as E. coli concentration increased. In soil and WCR-P models, the probability of the presence of Campylobacter significantly increased as E. coli population increased. These models provide a practical and effective way of evaluating the relationship between E. coli and foodborne pathogens and enable prediction of foodborne pathogen presence based on E. coli prevalence within the pastured poultry farm-to-fork continuum.

Fecal Microbiome Differences in Angus Steers with Differing Feed Efficiencies during the Feedlot-Finishing Phase.

The gastrointestinal microbiota of cattle is important for feedstuff degradation and feed efficiency determination. This study evaluated the fecal microbiome of Angus steers with distinct feed efficiencies during the feedlot-finishing phase. Angus steers (n = 65), fed a feedlot-finishing diet for 82 days, had growth performance metrics evaluated. Steers were ranked based upon residual feed intake (RFI), and the 5 lowest RFI (most efficient) and 5 highest RFI (least efficient) steers were selected for evaluation. Fecal samples were collected on 0-d and 82-d of the finishing period and microbial DNA was extracted and evaluated by 16S rRNA gene sequencing. During the feedlot trial, inefficient steers had decreased (p = 0.02) Ruminococcaceae populations and increased (p = 0.01) Clostridiaceae populations. Conversely, efficient steers had increased Peptostreptococcaceae (p = 0.03) and Turicibacteraceae (p = 0.01), and a trend for decreased Proteobacteria abundance (p = 0.096). Efficient steers had increased microbial richness and diversity during the feedlot period, which likely resulted in increased fiber-degrading enzymes in their hindgut, allowing them to extract more energy from the feed. Results suggest that cattle with better feed efficiency have greater diversity of hindgut microorganisms, resulting in more enzymes available for digestion, and improving energy harvest in the gut of efficient cattle.

Litter Commensal Bacteria Can Limit the Horizontal Gene Transfer of Antimicrobial Resistance to Salmonella in Chickens.

Fostering a "balanced" gut microbiome through the administration of beneficial microbes that can competitively exclude pathogens has gained a lot of attention and use in human and animal medicine. However, little is known about how microbes affect the horizontal gene transfer of antimicrobial resistance (AMR). To shed more light on this question, we challenged neonatal broiler chicks raised on reused broiler chicken litter-a complex environment made up of decomposing pine shavings, feces, uric acid, feathers, and feed-with Salmonella enterica serovar Heidelberg (S. Heidelberg), a model pathogen. Neonatal chicks challenged with S. Heidelberg and raised on reused litter were more resistant to S. Heidelberg cecal colonization than chicks grown on fresh litter. Furthermore, chicks grown on reused litter were at a lower risk of colonization with S. Heidelberg strains that encoded AMR on IncI1 plasmids. We used 16S rRNA gene sequencing and shotgun metagenomics to show that the major difference between chicks grown on fresh litter and those grown on reused litter was the microbiome harbored in the litter and ceca. The microbiome of reused litter samples was more uniform and enriched in functional pathways related to the biosynthesis of organic and antimicrobial molecules than that in fresh litter samples. We found that Escherichia coli was the main reservoir of plasmids encoding AMR and that the IncI1 plasmid was maintained at a significantly lower copy per cell in reused litter compared to fresh litter. These findings support the notion that commensal bacteria play an integral role in the horizontal transfer of plasmids encoding AMR to pathogens like Salmonella. IMPORTANCE Antimicrobial resistance spread is a worldwide health challenge, stemming in large part from the ability of microorganisms to share their genetic material through horizontal gene transfer. To address this issue, many countries and international organizations have adopted a One Health approach to curtail the proliferation of antimicrobial-resistant bacteria. This includes the removal and reduction of antibiotics used in food animal production and the development of alternatives to antibiotics. However, there is still a significant knowledge gap in our understanding of how resistance spreads in the absence of antibiotic selection and the role commensal bacteria play in reducing antibiotic resistance transfer. In this study, we show that commensal bacteria play a key role in reducing the horizontal gene transfer of antibiotic resistance to Salmonella, provide the identity of the bacterial species that potentially perform this function in broiler chickens, and also postulate the mechanism involved.

Communicating the utility of the microbiome and bioinformatics to small flock poultry producers.

The use of "omics" has become widespread across poultry production, from breeding to management to bird health to food safety and everywhere in between.  While the conventional poultry industry has become more exposed to the power and utility of "omic" technologies, smaller poultry flock producers typically do not have this same level of experience. Because smaller, nonconventional poultry production is a growing portion of the overall poultry market, it is important that they also have educational access to these research tools and the resultant data. While small flock producers are dedicated and knowledgeable farmers, their knowledge of these newer technologies may be limited at best, and it is the task of academic researchers to communicate the importance of these "omic" tools and how the omic data can improve a variety of different aspects of their operations. This review discusses ways to effectively communicate complex microbiota and microbial genome sequence data to small flock producers and transforming this data into meaningful and applicable information that they can utilize to inform beneficial management decisions.

Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies.

Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics-in addition to the traditional considerations when conducting an animal science study-makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type.

Serotype Screening of Salmonella enterica Subspecies I by Intergenic Sequence Ribotyping (ISR): Critical Updates.

(1) Background: Foodborne illness from Salmonella enterica subspecies I is most associated with approximately 32 out of 1600 serotypes. While whole genome sequencing and other nucleic acid-based methods are preferred for serotyping, they require expertise in bioinformatics and often submission to an external agency. Intergenic Sequence Ribotyping (ISR) assigns serotype to Salmonella in coordination with information freely available at the National Center for Biotechnology Information. ISR requires updating because it was developed from 26 genomes while there are now currently 1804 genomes and 1685 plasmids. (2) Methods: Serotypes available for sequencing were analyzed by ISR to confirm primer efficacy and to identify any issues in application. Differences between the 2012 and 2022 ISR database were tabulated, nomenclature edited, and instances of multiple serotypes aligning to a single ISR were examined. (3) Results: The 2022 ISR database has 268 sequences and 40 of these were assigned new NCBI accession numbers that were not previously available. Extending boundaries of sequences resolved hdfR cross-alignment and reduced multiplicity of alignment for 37 ISRs. Comparison of gene cyaA sequences and some cell surface epitopes provided evidence that homologous recombination was potentially impacting results for this subset. There were 99 sequences that still had no match with an NCBI submission. (4) The 2022 ISR database is available for use as a serotype screening method for Salmonella enterica subspecies I. Finding that 36.9% of the sequences in the ISR database still have no match within the NCBI Salmonella enterica database suggests that there is more genomic heterogeneity yet to characterize.

The Impact of Pre-Slaughter Fasting on the Ruminal Microbial Population of Commercial Angus Steers.

Diet impacts the composition of the ruminal microbiota; however, prior to slaughter, cattle are fasted, which may change the ruminal microbial ecosystem structure and lead to dysbiosis. The objective of this study was to determine changes occurring in the rumen after pre-slaughter fasting, which can allow harmful pathogens an opportunity to establish in the rumen. Ruminal samples were collected before and after pre-slaughter fasting from seventeen commercial Angus steers. DNA extraction and 16S rRNA gene sequencing were performed to determine the ruminal microbiota, as well as volatile fatty acid (VFA) concentrations. Microbial richness (Chao 1 index), evenness, and Shannon diversity index all increased after fasting (p ≤ 0.040). During fasting, the two predominant families Prevotellaceae and Ruminococcaceae decreased (p ≤ 0.029), whereas the remaining minor families increased (p < 0.001). Fasting increased Blautia and Methanosphaera (p ≤ 0.003), while Campylobacter and Treponema tended to increase (p ≤ 0.086). Butyrate concentration tended to decrease (p = 0.068) after fasting. The present findings support that fasting causes ruminal nutrient depletion resulting in dysbiosis, allowing opportunistic pathogens to exploit the void in the ruminal ecological niche.

AT Homopolymer Strings in Salmonella enterica Subspecies I Contribute to Speciation and Serovar Diversity.

Adenine and thymine homopolymer strings of at least 8 nucleotides (AT 8+mers) were characterized in Salmonella enterica subspecies I. The motif differed between other taxonomic classes but not between Salmonella enterica serovars. The motif in plasmids was possibly associated with serovar. Approximately 12.3% of the S. enterica motif loci had mutations. Mutability of AT 8+mers suggests that genomes undergo frequent repair to maintain optimal gene content, and that the motif facilitates self-recognition; in addition, serovar diversity is associated with plasmid content. A theory that genome regeneration accounts for both persistence of predominant Salmonella serovars and serovar diversity provides a new framework for investigating root causes of foodborne illness.